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CEBinaryAccuracyEvaluator_qnli-dev_results.csv ADDED
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+ epoch,steps,Accuracy
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+ 0,-1,0.9082921471718836
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+ 1,-1,0.9284276038806517
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+ 2,-1,0.9271462566355483
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+ 3,-1,0.9289767526999817
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+ 4,-1,0.9320885960095185
README.md ADDED
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+ # Cross-Encoder for Quora Duplicate Questions Detection
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+ This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
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+
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+ ## Training Data
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+ Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the [GLUE QNLI](https://arxiv.org/abs/1804.07461) dataset, which transformed the [SQuAD dataset](https://rajpurkar.github.io/SQuAD-explorer/) into an NLI task.
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+
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+ ## Performance
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+ For performance results of this model, see [SBERT.net Pre-trained Cross-Encoder][https://www.sbert.net/docs/pretrained_cross-encoders.html].
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+
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+ ## Usage
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+
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+ Pre-trained models can be used like this:
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+ ```python
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+ from sentence_transformers import CrossEncoder
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+ model = CrossEncoder('model_name')
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+ scores = model.predict([('Query1', 'Paragraph1'), ('Query2', 'Paragraph2')])
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+
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+ #e.g.
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+ scores = model.predict([('How many people live in Berlin?', 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'), ('What is the size of New York?', 'New York City is famous for the Metropolitan Museum of Art.')])
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+ ```
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+
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+ ## Usage with Transformers AutoModel
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+ You can use the model also directly with Transformers library (without SentenceTransformers library):
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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+ import torch
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+
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+ model = AutoModelForSequenceClassification.from_pretrained('model_name')
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+ tokenizer = AutoTokenizer.from_pretrained('model_name')
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+
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+ features = tokenizer(['How many people live in Berlin?', 'What is the size of New York?'], ['Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
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+
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+ model.eval()
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+ with torch.no_grad():
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+ scores = torch.nn.functional.sigmoid(model(**features).logits)
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+ print(scores)
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+ ```
config.json ADDED
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+ {
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+ "_name_or_path": "google/electra-base-discriminator",
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+ "architectures": [
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+ "ElectraForSequenceClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "embedding_size": 768,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "LABEL_0"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "label2id": {
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+ "LABEL_0": 0
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "electra",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "summary_activation": "gelu",
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+ "summary_last_dropout": 0.1,
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+ "summary_type": "first",
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+ "summary_use_proj": true,
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+ "type_vocab_size": 2,
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+ "vocab_size": 30522
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+ }
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special_tokens_map.json ADDED
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+ {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
tokenizer_config.json ADDED
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+ {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "name_or_path": "google/electra-base-discriminator"}
vocab.txt ADDED
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